NBER WORKING PAPER SERIES

DISPARITIES IN VULNERABILITY TO SEVERE COMPLICATIONS FROM COVID-19 IN THE UNITED STATES

Emily E. WiemersScott AbrahamsMarwa AlFakhriV. Joseph Hotz

Robert F. SchoeniJudith A. Seltzer

Working Paper 27294http://www.nber.org/papers/w27294

NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

Cambridge, MA 02138June 2020

This paper was prepared with support, in part, from the Aging Studies In-stitute at Syracuse University, the Duke Center for Population Health and Aging, which receives core support (P30AG034424) from the National Institute on Aging, and by the California Center for Popula-tion Research at the University of California at Los Angeles, which receives core support (P2C-HD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The collection of data used in this study was partly supported by the National Institutes of Health under grant number R01 HD069609 and R01 AG040213, and the National Science Foundation under award numbers SES 1157698 and 1623684. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

© 2020 by Emily E. Wiemers, Scott Abrahams, Marwa AlFakhri, V. Joseph Hotz, Robert F. Schoeni, and Judith A. Seltzer. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

Disparities in Vulnerability to Severe Complications from COVID-19 in the United StatesEmily E. Wiemers, Scott Abrahams, Marwa AlFakhri, V. Joseph Hotz, Robert F. Schoeni,and Judith A. SeltzerNBER Working Paper No. 27294June 2020JEL No. I1,I14,I24,J1,J11,J14

ABSTRACT

This paper provides the first nationally representative estimates of vulnerability to severe com-plications from COVID-19 overall and across race-ethnicity and socioeconomic status. We use the Panel Study of Income Dynamics (PSID) to examine the prevalence of specific health condi-tions associated with complications from COVID-19 and to calculate, for each individual, an index of the risk of severe complications from respiratory infections developed by DeCaprio et al. (2020). We show large disparities across race-ethnicity and socioeconomic status in the prev-alence of conditions which are associated with the risk of severe complications from COVID-19. Moreover, we show that these disparities emerge early in life, prior to age 65, leading to higher vulnerability to such complications. While vulnerability is highest among older adults regardless of their race-ethnicity or socioeconomic status, our results suggest particular attention should also be given to the risk of adverse outcomes in midlife for non-Hispanic Blacks, adults with a high school degree or less, and low-income Americans.

Emily E. WiemersDepartment of Public Administrationand International AffairsSyracuse UniversitySyracuse, NY 13244eewiemer@syr.edu

Scott AbrahamsDuke University Department of Economics 419 Chapel Drive 213 Social Sciences Bldg.Durham, NC 27708scott.abrahams@duke.edu

Marwa AlFakhriDuke UniversitySanford School of Public Policy201 Science DrDurham, NC 27708m.alfakhri@duke.edu

V. Joseph HotzDepartment of EconomicsBox 90097Duke UniversityDurham, NC 27708-0097and NBERv.joseph.hotz@duke.edu

Robert F. SchoeniInstitute for Social ResearchUniversity of Michigan426 Thompson St.Ann Arbor, MI 48109bschoeni@umich.edu

Judith A. SeltzerDepartment of SociologyUniversity of California, Los AngelesLos Angeles, CA 90095seltzerj@ucla.edu

1

The presence of preexisting health conditions increases vulnerability to severe complica-

tions from COVID-19 (1). The CDC reports that over 90% of hospitalized patients have at least

one preexisting health condition (2). In hospitals in New York City, Long Island, and Westchester

County, NY, 94% of patients hospitalized had at least one preexisting condition, 88% had more

than one, and the median number was four (3). The most common conditions among patients hos-

pitalized for COVID-19 are hypertension (3-13), diabetes, cardiovascular disease, kidney disease,

and obesity (3, 6-12). Conditional on hospitalization, those with preexisting conditions suffer more

severe outcomes (1, 6, 10-12) and experience higher fatality rates in China and the United States

(1, 11, 14).

In the United States, vulnerability to COVID-19 based on preexisting health conditions,

collides with long-standing disparities in health and mortality by race-ethnicity and socioeconomic

status, especially in midlife (15-17). Recent studies have highlighted these disparities finding that

Blacks, and the socioeconomically disadvantaged are more likely to have at least one health con-

dition that makes them vulnerable to COVID-19 (18, 19). Indeed, COVID-19 hospitalization rates

are higher for Blacks, and Hispanics while Whites are underrepresented relative to their population

percentages (20, 21). Differences in mortality rates by race-ethnicity are staggering: in Washington

D.C., African Americans make up 45% of the population yet suffer 76% of deaths (22). Yet what

is lacking is a means of translating the well-known disparities in a multiplicity of health conditions

into predictions about how the likelihood of having serious complications from COVID-19 varies

across race-ethnicity and socioeconomic status. As the pandemic continues, the inability to trans-

late health disparities into vulnerability to COVID-19 hampers our ability to be forward-looking

about differential impact of the virus.

This paper leverages a recent machine learning model of the likelihood of hospitalization

for several respiratory conditions (23). We apply the model to preexisting health conditions re-

ported in the Panel Study of Income Dynamics (PSID) which allows us to form the first population-

representative estimates of vulnerability to in-patient hospitalization for respiratory conditions for

the U.S. and how vulnerability varies by education, income, and race-ethnicity. As the vulnerabil-

ity index is itself a function of preexisting health conditions, we also examine the distribution of

such conditions across demographic groups.

We find that disparities in preexisting health conditions across race-ethnicity and socioec-

onomic status translate into large gaps in vulnerability. Adults with a high school education or less

2

are 1.6 times as likely as those with a college degree to face severe complications and Americans

in the lowest income quartile face 2.7 times the risk compared to those in the highest quartile.

These gaps are due to preexisting health conditions that emerge earlier in life for less-educated and

low-income adults. We also find large gaps in vulnerability appearing in midlife for non-Hispanic

Blacks, whereas Hispanics face lower risks based on preexisting health conditions and age. We

show that to understand disparities in vulnerability to severe complications from COVID-19, it is

crucial to describe how disparities in the age pattern of specific preexisting conditions and the

presence of multiple conditions would account for disparities in the incidence of severe complica-

tions from the COVID-19 pandemic in the U.S.

We also show that while disparities in preexisting health do predict disparities in compli-

cations from COVID-19, our predictions do not fully track the disparities in observed hospitaliza-

tion from the virus. The differences in our predictions and actual hospitalizations suggests that

there are differences by race-ethnicity and socioeconomic status in exposure to the coronavirus,

the likelihood of infection given exposure, and in the complications from infections that go beyond

the disparities in preexisting health. Disparities in exposure based on residential and occupational

characteristics have been documented during the COVID-19 pandemic (6, 7). The persistence of

race-ethnic differences in hospitalization rates for COVID-19 after controlling for comorbidities

suggests there may be disparities in timely access to care (24). However, understanding variation

in vulnerability based on preexisting health conditions is crucial for predicting the likely disparities

in the effect of the disease and for assessing the role of factors beyond health conditions. For these

tasks, population-representative estimates are essential. While we focus on disparities in preexist-

ing health conditions and their implications for vulnerability to severe complications, our work is

a complement to studies of disparities in exposure to the coronavirus (25-27) and in inequities in

health care (24, 28, 29).

MATERIALS AND METHODS

In this section we discuss the data and methods used to analyze the preexisting health con-

ditions that the CDC and the developing literature indicate are risk factors for severe illness from

COVID-19. We begin with a discussion of the Panel Study of Income Dynamics (PSID) data that

we use to determine the distribution of and disparities in risk factors in the adult U.S. population.

We also describe the index of relative vulnerability to severe illness from respiratory infections

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developed by ClosedLoop.ai (23), which is a function of preexisting conditions, recent hospitali-

zation, and age. We discuss the construction of the index and how we apply it to nationally repre-

sentative data.

Panel Study of Income Dynamics The PSID is a national longitudinal survey that has interviewed the original sample mem-

bers and their descendants since 1968. The original sample included oversamples of Black and

low-income families to facilitate the study of poverty. With the addition in 1997 of a sample of

immigrants who arrived in the United States after 1968 plus the addition in 2017 of a sample of

immigrants who arrived after 1997, the 2017 wave includes 26,455 individuals representative of

the national population when weighted (30). We use the 2017 data on adults ages 25 and older, a

sample of 13,150 household heads and spouses (or cohabiting partners)1 who are not missing data

on age, race-ethnicity, educational attainment, household income, or preexisting health condi-

tions.2 Throughout our analyses, we apply the 2017 PSID cross-sectional weights.

Demographics and Socioeconomic Status Race-ethnicity is classified as Hispanic, non-Hispanic Black, non-Hispanic White, and

non-Hispanic other. Because of sample size we do not report separate estimates for individuals

who are non-Hispanic other race. Education is categorized as no more than 12 years (high

school/GED or less), 13-15 years (some college), and at least 16 years (BA degree or more). In-

come is measured at the household level. We classify each adult according to the income quartile

of their household.3

The population characteristics of our sample are displayed in Table 1. We show the distri-

butions of gender, race-ethnicity, educational attainment, and household income quartile overall

and for three age groups (25-44, 45-64 and 65+). We also show the distribution of age across each

race-ethnic, educational attainment, and household income category. In Appendix A, we compare

1 PSID defines a cohabiting partner is one who has lived with the head for one year or more. Since 2017, the PSID has referred to heads as “reference person 1” and spouses and cohabiters as “reference person 2.” For ease in comparison to past work, we continue to use “head” and “spouse.” 2 There are 13,831 heads and spouses age 25 and older. We dropped 681 due to missing data, 302 due to missing data on race-ethnicity, education, or income and a further 379 due to missing data about health conditions, producing a sample of 13,150. 3 The lowest quartile consisted of those households with reported incomes between $0 and $29,096; the second quar-tile, $29,096 and $56,760; the third quartile, $56,760 and $101,820; and the top quartile, incomes greater than $101,820. The measure is based on reports about the income received in 2016 and is reported in 2016 dollars.

4

the PSID data to that of the Current Population Survey (CPS) from the 2017 Annual Social and

Economic Supplement (ASEC). Overall, the samples produce almost identical distributions.4 Prior

work has shown that estimates of income from the PSID match well with those from the March

CPS (31).

Differences in the age distribution across demographic and socioeconomic groups matter

because the risks from COVID-19 vary with age. Females are one year older, which is consistent

with well-documented higher mortality rates for men. Non-Hispanic Whites (NH White) make up

71% of the adult U.S. population, while 11% are Non-Hispanic Blacks (NH Blacks), 13% are

Hispanics and 5% are other non-Hispanic groups. NH Whites are the oldest on average (53.6 years

old), followed by NH Blacks (49.2), and Hispanics are the youngest at 46.5 years. Among NH

Whites, 28% are 65 and older compared to only 17% of NH Blacks, and 13% of Hispanics. With

respect to educational attainment, 38% of people have a high school degree or less, 24% have

completed some college, and 38% have a BA degree or higher. Due to educational differences

across birth cohorts, those with a high school degree or less are older (54.3 years old) than those

with some college (51.2) or those with a BA (50.1). Finally, 18% of individuals in the adult pop-

ulation are in the lowest quartile of household income, while 23%, 28% and 32% are in the second,

third and top quartiles, respectively. Again, we see age differences across these quartiles, with

those in the lowest quartile being older (56.6 years) compared to the top two quartiles, which

average 50.4 years.

Self-Reported Health Conditions (Risk Factors) The PSID includes questions about whether a person was ever told by a health professional

they have certain health conditions. Many of these conditions are listed by the CDC as risk factors

for becoming severely ill from infection of COVID-19. The health conditions (risk factors) in the

PSID include asthma, diabetes, heart disease, heart attack, hypertension, lung disease, neurological

conditions, cancer, stroke, and kidney disease. In addition, the PSID asks respondents whether

they were hospitalized in the previous year. Past hospitalization is a risk factor in the DeCaprio et

al. model described below. Finally, height and weight are reported, which we use to compute each

4 There are slight differences between the PSID and CPS samples for the distributions of educational attainment, presumably because of slightly different questions and their wording.

5

respondent’s BMI. We then create an indicator for severe obesity (BMI ≥ 40). Appendix B de-

scribes how estimates of the prevalence of self-reported health conditions in the PSID compare to

those in other data sources. Appendix Table B-1 shows the question wording for all self-reported

health conditions in the PSID.

Relative Vulnerability Index According to the CDC, the risk of severe complications from COVID-19 resulting in hos-

pitalization increases with a number of preexisting health conditions, i.e., risk factors or comor-

bidities, and with age, but the size of the effect of each risk factor is currently unknown (2). To

circumvent the lack of current data on these effects, DeCaprio et al. (23) assessed the risk of hos-

pitalization for respiratory infections available in existing medical claims data. In particular, they

looked at (in-patient) hospitalizations associated with: acute respiratory distress syndrome, pneu-

monia (other than caused by tuberculosis), influenza, acute bronchitis and other upper respiratory

infections. DeCaprio et al. used medical claims data on hospitalizations for Medicare recipients –

using the Center for Medicare and Medicaid Services Limited Data Set, which contains a 5% sam-

ple of the Medicare population – and from Healthfirst, a New York, non-Medicare population

insurer. They mapped in-patient hospitalizations from these respiratory infections to individual-

level data on 11 preexisting health conditions (risk factors) coded in the Medicare and Healthfirst

data, along with patients’ gender, age and whether they had been hospitalized in the last three

months.

As described in DeCaprio et al. (23), the authors estimated several alternative versions of

their model. In our paper, we use their “Survey Model,” which is based on a logistic regression of

incidence of hospitalizations due to the above conditions and is suitable for use with the health

conditions available in the PSID.5 The model is trained on over 2 million individuals.

The model is of the following form. Let 𝑑𝑑𝑖𝑖 denote a 0/1 indicator of whether individual i

is hospitalized with a severe respiratory infection. Then,

(1) 𝑝𝑝𝑖𝑖 ≡ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃(𝑑𝑑𝑖𝑖 = 1) = exp (𝑉𝑉𝑉𝑉𝑖𝑖∗) [1 + exp(𝑉𝑉𝑉𝑉𝑖𝑖∗)]⁄ ,

where 𝑑𝑑𝑖𝑖 is a 0/1 indicator for whether i is hospitalized and 𝑉𝑉𝑉𝑉𝑖𝑖 denotes individual i’s Vulnerability

Index score which is given by the following function:

5 At https://closedloop.ai/c19index/ one can calculate values of what they refer to as the “C-19index” based on risk factors, hospitalization experience and age. The Survey Model that we use is similar, but not exactly the same, as the web model is trained on additional information that we do not use, e.g., zip-code.

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(2) 𝑉𝑉𝑉𝑉𝑖𝑖∗ = 𝛽𝛽0 + 𝛽𝛽1𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 + 𝛽𝛽2𝑀𝑀𝑀𝑀𝑀𝑀𝐴𝐴𝑖𝑖 + 𝛽𝛽3𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑝𝑝𝑖𝑖 + 𝜷𝜷4𝑹𝑹𝑖𝑖 + 𝜷𝜷5𝑹𝑹𝑖𝑖 ∙ 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖,

where 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 is i’s current age, 𝑀𝑀𝑀𝑀𝑀𝑀𝐴𝐴𝑖𝑖 is a 0/1 indicator of whether i is a male; 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑝𝑝𝑖𝑖 is a 0/1

indicator of whether i has been recently hospitalized, and 𝑹𝑹𝑖𝑖 is a vector of 0/1 indicators for

whether i has particular health conditions, or risk factors, 𝑅𝑅𝑘𝑘𝑖𝑖 k, k = 1,…, K. Given the logistic

form of (1), it follows that 𝑉𝑉𝑉𝑉𝑖𝑖∗ is the log odds of severe illness for individual i that is a function of

their age, gender, recent hospitalization and health-related risk factors. It follows that

(3) 𝑉𝑉𝑉𝑉𝚤𝚤� = exp (𝑉𝑉𝑉𝑉𝑖𝑖∗)

is their odds ratio of risk for a severe illness or the relative vulnerability index score for individual

i.

Given the expression for 𝑉𝑉𝑉𝑉𝑖𝑖∗ in (2), the base group for the DeCaprio et al. model is a female

with no preexisting risk factors and age equal to 0. But, to provide a more meaningful base group,

we use the following relative vulnerability index score:

(4) 𝑉𝑉𝑉𝑉𝑖𝑖 = exp (𝑉𝑉𝑉𝑉𝑖𝑖∗ − 𝛽𝛽0 − 𝛽𝛽1 ∙ 30)

so 𝑉𝑉𝑉𝑉𝑖𝑖 provides the odds that individual i has of a severe illness relative to that for a 30-year-old

female with no risk factors. We report average 𝑉𝑉𝑉𝑉𝑖𝑖 by age, race-ethnicity, education, and household

income.

In Appendix Table B-2, we display, in the first two columns, respectively, the risk factors

in 𝑹𝑹𝑖𝑖 for the DeCaprio et al. model and estimates of the 𝛽𝛽𝑗𝑗’s for 𝑉𝑉𝑉𝑉𝑖𝑖∗ in (2). The third column in

Appendix Table B-1 indicates which health risk factors are available in the PSID. As one can see,

the PSID does not measure some risk factors included in the DeCaprio et al. model. In our imple-

mentation of the vulnerability index with PSID data, we do not attempt to adjust the formula for

absent variables; rather, we act as if the members of the PSID sample do not have these conditions.

Because of this, the values of the vulnerability index scores for our sample will be biased down-

wards. As discussed in Appendix B, almost all of the risk factors used in the DeCaprio et al. model

that are not available in the PSID, with the exception of liver disease (cirrhosis of the liver), either

have very low marginal impacts on the vulnerability index (based on the coefficient estimates for

these conditions) or the incidence of the risk factor is very low in the U.S. population.

Finally, to demonstrate how the various risk factors affect the relative vulnerability index

scores, we present, in Figure 1, the age profiles of the marginal contributions of each risk factor to

relative vulnerability. The marginal contribution at age, 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖, for risk factor, 𝑅𝑅𝑘𝑘𝑖𝑖, is given by:

7

(5) 𝜕𝜕𝑉𝑉𝑉𝑉𝑖𝑖𝑅𝑅𝑘𝑘𝑖𝑖

= (𝛽𝛽4𝑘𝑘 + 𝛽𝛽5𝑘𝑘𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖) ∙ exp (𝛽𝛽1[𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 − 30] + 𝛽𝛽4𝑘𝑘 + 𝛽𝛽5𝑘𝑘𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖),

where we evaluate all of the other risk factors, 𝑅𝑅𝑚𝑚𝑖𝑖 ,𝑚𝑚 ≠ 𝑘𝑘, at 0.

In Figure 1, we draw attention to those risk factors that have the most sizeable contributions

to vulnerability at various ages by labeling them in the figure. As one can see from Figure 1,

hypertension and lung disease have the largest impacts on the relative vulnerability index at all

ages, and both effects rise rapidly with age. Severe obesity – and, to a lesser extent, cancer – also

consistently have a large impact on the vulnerability index at all ages. In early adulthood (25-44),

asthma contributes a sizeable impact on relative vulnerability, but this risk factor is less important

at midlife (45-64) and older ages (65+). With age, one also sees that heart disease and heart attacks

contribute to relative vulnerability. At older ages (65+), neurological conditions start to have sub-

stantial impacts on relative vulnerability. Finally, recent hospitalizations, which presumably proxy

for compromised health, have larger impacts on relative vulnerability as individuals age.

All of the other risk factors have more modest impacts, raising the vulnerability odds of

severe disease relative to a 30-year old female with no risk factors 2 to 5 times and do not vary

substantially with age. Nonetheless, the DeCaprio et al. model implies that the increase in relative

vulnerability to severe illness will result, in large part, from the number of conditions one has at

any age.

Three final points about Figure 1. First, age, per se, has a very small impact on relative

vulnerability. That is, growing older, when one has no risk factors, has virtually no impact on

vulnerability. It is the accumulation of risk factors and the interactions of these risk factors with

age that lead to higher vulnerability at older ages. Second, a male, who has no other health-related

risk factors, is at slightly higher vulnerability to severe illness compared to females and this male-

female differential in vulnerability rises modestly with age, so that, by age 72, a male’s vulnera-

bility, relative to a 30-year old female, is 1.14 times higher. Third, the distribution of and disparities

in the relative vulnerability to severe illness from COVID-19 in the U.S. adult population reported

below will be driven by the marginal contributions of individual risk factors displayed in Figure 1

based on the DeCaprio et al. model and by the prevalence of these risk factors in the population.

RESULTS Prevalence of Health-Related Risk Factors in the United States and their Disparities In Table 2, we present the prevalence of the risk factors and how they vary by age, demo-

8

graphic group and socioeconomic status. We test whether differences within each group are sta-

tistically significant from the base category (age 25-44, NH White, BA or more, top income quar-

tile) and denote these with asterisks.

The risk factors are listed in order of their prevalence in the overall U.S. adult population

based on the PSID data. Hypertension is the most prevalent factor, at 33%. Hypertension increases

with age, with 59% of those 65 and older having been diagnosed. Diabetes and asthma are the

second and third most prevalent risk factors at 12% and 11%, respectively. Diabetes increases with

age, with only 4% of those in early adulthood (25-44 years old) having been diagnosed while one

quarter (23%) of those 65 and older have the disease. In contrast, asthma declines with age, going

from 12% for those 25-44 years old to 9% for those 65 and older. Some 9% of the adult population

has been diagnosed with cancer and its prevalence rises with age up to 1 in 5 (21%) of those 65

and older. Five percent of adults are severely obese and there is little variation by age. Among the

remaining health-related risk factors, the prevalence ranges from 1% to 6%, all increasing with

age. Finally, 10% of adults report being hospitalized in the preceding year for one night or more,

including 17% of those 65 and older.

As the other columns in Table 2 make clear, there is an unequal distribution of these health-

related risk factors by race-ethnicity, educational attainment, and income. Compared to NH

Whites, NH Blacks have higher prevalence of most of the risk factors for COVID-19, with nearly

all of these differences being statistically significant. For example, NH Blacks are one third more

likely to have been diagnosed with hypertension than NH Whites (44% vs 33%). Cancer is more

prevalent for NH Whites than NH Blacks (10% for NH Whites vs 6% for NH Blacks). Neurolog-

ical conditions are also more prevalent for NH Whites (3%) than NH Blacks (2%). Prevalence

rates for the risk factors listed in Table 2 are the same or lower for Hispanic adults than NH Whites

(or NH Blacks), except for diabetes, which is more prevalent for Hispanics than NH Whites. The

prevalence of hypertension is 23% for Hispanics compared to 33% for NH Whites. These race-

ethnic differences have been documented in prior studies (32-34).

The prevalence of risk factors is also quite different by education. Adults in the United

States with no more than a high school education have higher rates for almost all risk factors com-

pared to adults with a college degree. For example, 39% suffer from hypertension compared to

one quarter (26%) of the higher educated group. Two notable exceptions to this pattern are cancer

and chronic kidney disease, which do not vary by education.

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Adults in the bottom quartile of household income have higher rates for every risk factor

compared to those in the top quartile, and these differences are sizeable. Compared to those in the

top income quartile, those in the bottom quartile are about twice as likely to have been diagnosed

with hypertension (25% vs 46%), diabetes (8% vs 18%) and asthma (9% vs 15%), are about three

times more likely to have had a heart attack (2% vs 7%) or a stroke (2% vs 7%) and are six times

more likely to suffer from lung disease (2% vs 12%). Those in the lowest income group are three

times more likely to have had an in-patient hospitalization in the past year (6% vs 18%).

Table 2 also shows that the prevalence of nearly all risk factors increases with age. The

total number of risk factors also increases with age. In Figure 2, we display the percent of the

population with 0, 1, 2 and 3 or more of the risk factors listed in Table 2 across three age groups.

Among all adults, 47% have 0 risk factors, while 13% have 2 and another 13% have 3 or more, for

an average of 1.02 risk factors in the U.S. adult population. The number of risk factors increases

across age groups, with 64% of adults 25-44 having no risk factors while only 20% of those 65

and older having none. Only 3% of younger adults have 3 or more risk factors, while 28% of those

65 and older have 3 or more.

We showed that the distribution of age varies across race-ethnicity, education, and income

groups in our sample. Appendix Tables C-1 - C-3 report the prevalence of each of the risk factors

by race-ethnicity, socioeconomic status, and age. Differences in the prevalence of hypertension

between NH Blacks and NH Whites and by education and income emerge early in life and are

large by midlife. Hispanics have a health advantage in hypertension in midlife but the prevalence

of diabetes among Hispanics is greater than for NH Whites at ages 45-64. There are disparities

across nearly all conditions for less-educated and low-income Americans that are similar to those

for NH Blacks. These disparities become large in midlife though there are statistically significant

differences by education and income in the prevalence of nearly all health conditions among those

age 25-44.

In Figure 3, we present the distribution of the number of risk factors for the overall popu-

lation and by age across race-ethnic groups, educational attainment, and household income, using

the same format as Figure 2. Consistent with the disparities by race-ethnicity in individual risk

factors, among all adults, NH Blacks are more likely to have three or more risk factors compared

to NH Whites (18% vs 13%), while only 7% of Hispanics have such a high number (Figure 3(a),

25+ age group). Among adults with 12 years of schooling or less, 17% have 3 or more risk factors,

10

compared to just 7% of those with a college degree (Figure 3(b), 25+ age group). Finally, the

differences in numbers of risk factors are particularly dramatic by income, with 24% of adults in

the bottom quartile of household income having 3 or more risk factors compared to only 6% among

those in the top income quartile (Figure 3(c), 25+ age group). All of the differences highlighted

above are statistically significant.

Figure 3 also shows the distribution of the number of risk factors by age across race-eth-

nicity, education, and household income. For NH Blacks, those with 12 years of schooling or less,

and those in the bottom quartile of income, risk factors accumulate at earlier ages. For example,

by age 45-64, 41% of NH Blacks have two or more risk factors compared to only 25% of NH

Whites. On average, fewer adults age 45-64 with household incomes in the top income quartile

have two or more conditions (16%) than those age 25-44 in the bottom income quartile (19%), and

fewer adults in the oldest group with household incomes in the top quartile have two or more

conditions (38%) than those in middle age (45-64) with incomes in the bottom quartile (45%). All

of these differences highlight above are statistically significant.

Disparities in Relative Vulnerability to Severe Illness in the United States

In Figure 4, we display median values of the relative vulnerability index by numbers of

risk factors for the three age groups and the overall adult population.6 Recall that the index

measures the odds of having severe illness from COVID-19 relative to a 30 year-old female with

no risk factors. The median value is 4.08 (not shown in Figure 4). As seen in Figure 4, relative

vulnerability varies substantially by number of risk factors and by age. Among all ages (the 25+

group), those with no risk factors are 1.9 times more vulnerable compared to a 30 year-old female.

As the number of risk factors increases, relative vulnerability increases, with the median risk of

individuals with 3 or more risk factors at 47.9 times greater odds of becoming severely ill. On

average, individuals in this high-risk group have 3.8 risk factors and 9% have 5 or more risk fac-

tors. Individuals with particular risk factors are also likely to have more risk factors. For example,

adults with hypertension had, on average, 1.2 other risk factors, while those who did not have

hypertension had an average of 0.4 risk factors.

Figure 4 also makes clear that the increasing numbers of risk factors for those at older ages

displayed in Figure 2, result in a progressive increase in relative vulnerability across age groups.

6 Similar patterns emerge when using means and medians.

11

For example, individuals who are 65 and older and who have 3 or more risk factors are 70 times

more likely to become severely ill than a 30 year-old healthy (no risk factors) female. Among

individuals in this high-risk group, 25% have 5 or more risk factors and half are age 75 or older.

While this estimate of relative vulnerability is extraordinarily high, the relative magnitudes of the

differences by age and number of risk factors illustrate how the accumulation of risk factors with

age makes many older Americans much more vulnerable to COVID-19.

Figure 4 also illustrates how having multiple risk factors heightens the vulnerability to

severe illness from COVID-19 at any age. For example, adults 65 and older with no risk factors

(20% of that age group) have a median vulnerability that is 5.6 times that of the reference person

(30 year-old female with no risk factors). This is almost the same relative vulnerability to severe

illness as for 25-44 year-olds with 2 risk factors (6.6 relative vulnerability and 7% of the younger

age group). In fact, the relative vulnerability of those 65 and older with no risk factors is more than

3 times lower than the relative vulnerability of 25-44 year-olds with 3 or more risk factors. This

comparison illustrates both the disturbing consequences of younger adults having multiple risk

factors and the apparent benefits to older age adults of having fewer risk factors with respect to

vulnerability of becoming severely ill from COVID-19.

The results in Figure 4 for relative vulnerability represent the differences in the distribu-

tions of numbers of risk factors for different age groups and the impact that particular risk factors

have on relative vulnerability at different ages. Recall for example that Figure 1 shows that hyper-

tension and lung disease have the largest impacts on the relative vulnerability scores at all ages

and that the impacts of both increased markedly with age. At the same time, as we noted in our

discussion of Table 2, the prevalence of hypertension in the U.S. population is high (33%) and

increases across age groups, while, relatively speaking, the prevalence of lung disease is much

lower (6%) and increases less markedly with age. Thus, the relative vulnerability index allows one

to characterize the combined impact of (and differences in) the prevalence of risk factors and their

impact on population-level vulnerability, providing greater insight than simply looking at the prev-

alence of risk factors can provide.

In Figure 5 we display the median relative vulnerability scores by age for race-ethnic, ed-

ucation, and income groups. Recall that in Figure 3(a) we found that NH Blacks have more risk

factors, on average, than NH Whites, and that NH Blacks have a higher number of risk factors

after age 45 than NH Whites. The higher prevalence of hypertension among NH Blacks contributes

12

to this race differences in the number of risk factors (see Appendix Table C-1). We also found that

the prevalence of most risk factors is lower among Hispanics, other than diabetes (see Appendix

Table C-1). But diabetes has a smaller effect on VI than hypertension (Figure 1). As shown in

Figure 5(a), by age 45, sizeable differences in relative vulnerability to severe illness emerge be-

tween NH Blacks and NH Whites and these gaps remain large at older ages. Hispanics have lower

relative vulnerability at all ages. Across the whole sample (25+), the difference in relative vulner-

ability between NH Blacks and NH Whites is muted. This is due to the age distributions shown in

Table 1 where those who are age 65 and older make up 28% of the population of NH Whites but

only 17% of the population of NH Blacks.

Figure 5(b) has the same format for showing the median relative vulnerability among adults

with a high school degree or less education and those who are college educated. On average, rela-

tive vulnerability is 60% higher for those with high school or less (5.4) than for those with a college

degree (3.3), and this gap by education emerges in midlife.

Finally, Figure 5(c) presents the median vulnerability for those in the bottom and top quar-

tiles of household income. Among all adults (ages 25+), there is nearly a 3-fold difference in rel-

ative vulnerability, with those in the bottom quartile of income having 9.4 higher odds of severe

illness compared to the reference person, while for those in the top income quartile, the odds are

only 3.4 times higher. This gap by income in relative vulnerability emerges among middle-age

adults and reflects large differences in prevalence of risk factors (Figure 3(c)) by income.

A full assessment of the relationships among race-ethnicity, education, and income is be-

yond the scope of this paper. But in light of the correlations among these dimensions of population

subgroups, we examined differences in vulnerability in midlife across race-ethnic groups within

education and income groups, the differences in vulnerability across education within race-ethnic

and income groups, and the differences in vulnerability across income within race-ethnic and ed-

ucation groups (see Appendix Table C-4). Taking account of socioeconomic differences mutes,

but does not eliminate the greater vulnerability of NH Blacks compared to NH Whites. Hispanics’

health advantage in vulnerability is pronounced among low-income adults. Education and income

differences in vulnerability remain large but are reduced within race-ethnic groups.

DISCUSSION

The DeCaprio et al. model predicts hospitalizations due to respiratory infections prior to

13

the COVID-19 outbreak and as a function of age and preexisting health risk factors. How accu-

rately does this model predict actual disparities by age, race-ethnicity, and socioeconomic status

in hospitalizations associated with the coronavirus? We address this question using early surveil-

lance data from the CDC.

Hospitalization is influenced by two channels: exposure and susceptibility to infection and

its complications. Each may differ in their determinants and, thus, may differ across particular

viruses and diseases. Susceptibility and complications are likely to depend heavily on the sort of

preexisting conditions included in the DeCaprio et al. model. Evidence from New York City indi-

cates that, conditional on testing positive for COVID-19, the comorbidities and the demographic

characteristics of patients hospitalized with COVID-19 are similar to those admitted with other

acute respiratory infections (11). However, exposure to COVID-19 likely differs by factors other

than age and preexisting health conditions. Separating these channels empirically is beyond the

scope of available data. However, we can compare the model’s predictions for disparities in vul-

nerability presented above with the distributions of available characteristics of those that have been

hospitalized to date with severe complications from the coronavirus.

Garg et al. (20) report on the prevalence of preexisting health conditions disaggregated by

age for laboratory-confirmed COVID-19 hospitalizations during March 2020. We predict VI based

on the average conditions of these hospitalized patients overall and by age group. We compare VI

based on the average preexisting conditions of hospitalized patients to the distribution of VI in the

PSID. Appendix D describes these calculations in more detail.

The VI of hospitalized patients of all ages is 8.51, which corresponds to the 66% percentile

of VI for the PSID sample. For hospitalized patients between 18-49 it is 3.04, for those age 50-64

it is 7.87, and for those 65 and older it is 23.14, corresponding to the 48th percentile, the 65th per-

centile, and the 84th percentile of the PSID vulnerability distribution, respectively. This corre-

spondence between the VI for actual COVID-19 hospitalizations and that in the PSID based on the

DeCaprio et al. model is consistent with our findings that older adults with multiple preexisting

conditions in the general population are more vulnerable to severe complications requiring hospi-

talization and that a substantial fraction of adults in midlife are also vulnerable to hospitalization.

For younger adults, our VI estimates based on health conditions correspond less well to actual

COVID-19 hospitalizations. This lack of correspondence suggests that, compared to older adults,

14

exposure to infection is relatively more important for younger adults than susceptibility to infec-

tion and complications.

The distinction between exposure and infection also sheds light on race-ethnic disparities

in observed hospitalizations. The CDC data indicate a dramatic over-representation of NH Blacks

among hospitalized patients while we find no NH Black-NH White difference in median VI when

all ages are combined (see Figure 5a) (21). Within age groups, we report that NH Blacks have

higher VI. These patterns are consistent with higher rates of exposure among NH Blacks than NH

Whites that are compounded by disparities in health conditions within age groups. Relative to NH

Whites, Hispanics are also overrepresented among patients hospitalized with COVID-19, both

overall and after adjusting for age (21). We report that Hispanics have lower median VI than NH

Whites both overall and within age groups, and thus rates of exposure are likely higher for His-

panics than NH Whites.

CONCLUSION

This paper provides the first nationally representative estimates of vulnerability to severe

complications from COVID-19 overall and across race-ethnicity and socioeconomic status. We

use the PSID to examine the prevalence of specific health conditions associated with complications

from COVID-19 and to calculate, for each individual, an index of the risk of severe complications

from respiratory infections developed by DeCaprio et al. (23) based on these health conditions.

We show large disparities across race-ethnicity and socioeconomic status in the prevalence of con-

ditions associated with adverse outcomes, and in the overall risk of severe complications. Moreo-

ver, we show that these disparities emerge early in life, prior to age 65.

Consistent with the surveillance data, our results suggest a higher risk of adverse outcomes

in midlife for non-Hispanic Blacks. Education and income are not included in the surveillance data

but our results further suggest that adults with a high school degree or less and low-income Amer-

icans are also at high risk of adverse outcomes starting in midlife. These results are especially

important as states and municipalities start to reopen businesses and public services as recent evi-

dence suggests that disadvantaged groups are less likely to be able to socially distance at work

(27). The evidence that we present shows large disparities in preexisting health conditions among

working-age adults for these same groups. Combined, these results suggest that localities with high

levels of poverty, a high percentage of African-Americans, or a high concentration of less-educated

adults face potentially devastating effects of the virus.

15

There are caveats to this study. First, we are likely understating the disparities in the risk

for severe complications from COVID-19 by race-ethnicity, education, and income based on

preexisting health conditions because disadvantaged populations have higher rates of undiagnosed

diseases (35-39), because socioeconomic status and race-ethnicity affect whether an individual is

hospitalized regardless of preexisting conditions (40), and because among those diagnosed with a

disease, rates of control for some diseases are lower for disadvantaged populations (36, 37, 39,

41).

Second, there is more work to be done exploring interactions of race-ethnicity, education,

and income. There is ample evidence of large mortality gaps across these groups (42), and this is

an area for more extensive exploration.

Finally, we have discussed why disparities in the risk of severe complications based on

preexisting health conditions does not capture the full risk of hospitalization and death from

COVID-19 because it does not account for factors influencing exposure to the virus or access to

high quality care. All of these factors, including the preexisting health conditions we examine, are

influenced by systemic inequalities in society and in our health care system. Such inequalities will

make it difficult to isolate the influence of health conditions on disparities in the overall effect of

COVID-19 pandemic on the U.S. population but starting to disentangle these effects is crucially

important.

16

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Table 1. Percent of Demographic and SES Groups by Age across Age Categories & Mean Age

25-44 45-64 65+ 25-44 45-64 65+

Percent, All Ages

(25+)Mean Age

Sample sizes

Overall 38 38 24 51.9 13,150

GenderFemale 37 38 26 51 52 55 52 52.4 7,140Male 39 38 23 49 48 45 48 51.5** 6,010

Race and EthncityNH White 33 38 28 64 72 83 71 53.6 7,082NH Black 42 41 17 12 12 7 11 49.2*** 4,158Hispanic 52 36 13 18 12 7 13 46.5*** 1,464NH Other 52 32 15 7 4 3 5 47.4*** 446

EducationHS or less 32 40 28 32 41 44 38 54.3*** 5,262Some college 39 38 23 25 24 22 24 51.2** 3,482BA or more 43 36 22 43 36 34 38 50.1 4,406

Household IncomeBottom Quartile 31 33 37 15 16 27 18 56.6*** 2,550Second Quartile 40 31 29 24 18 27 23 52.3*** 3,166Third Quartile 41 36 23 30 26 26 28 50.5 3,660Top Quartile 37 48 15 31 40 20 32 50.3 3,774Sample sizes 6,808 4,392 1,950

Percent across Age Categories Percent within Age Categories

Data Source: 2017 Wave, PSID. Sample: PSID heads and spouses, 25 years and older. Weights: PSID individual cross-section weights.Notes: Asterisks (*) are for tests of age of a subgroup being significantly different from base group, where: * P-value ≤ 0.10; ** P-value ≤ 0.05; *** P-value ≤ 0.01. Base groups are female, NH White, BA or more, top income quartile.

20

Table 2. Age, Race-Ethnicity, and SES in Prevalence (%) of Health-Related Risk Factors

Risk factor Overall 25-44 45-64 65+NH White

NH Black Hispanic

HS or less

BA or more

Bottom Quartile

Top Quartile

Hypertension 33 14 35*** 59*** 33 44*** 23*** 39*** 26 46*** 25Diabetes 12 4 14*** 23*** 11 16*** 14* 15*** 9 18*** 8Asthma 11 12 10** 9*** 11 13** 9** 11* 10 15*** 9Hospitalization 10 7 9** 17*** 10 12* 9 12*** 7 18*** 6Cancer 9 2 7*** 21*** 10 6*** 5*** 9 9 11** 8Lung disease 6 3 6*** 11*** 6 7 3*** 9*** 3 12*** 2Heart disease 6 1 5*** 14*** 6 7 2*** 7*** 4 11*** 3Severe obesity 5 5 6 4* 4 9*** 5 6*** 3 7*** 3Heart attack 4 1 4*** 10*** 5 5 2*** 6*** 2 7*** 2Stroke 3 1 3*** 8*** 3 6*** 2* 4*** 2 7*** 2Neurological conditions 3 1 2*** 5*** 3 2** 2*** 4*** 1 6*** 0Chronic Kidney Disorder 1 <1 1** 1*** 1 1 <1* 1 1 1 0Sample Sizes 13,150 6,808 4,392 1,950 7,082 4,158 1,464 5,262 4,406 2,550 3,774

Notes: Asterisks (*) are for tests of risk factors of a subgroup being significantly different from base group, where: * P-value ≤ 0.10; ** P-value ≤ 0.05; *** P-value ≤ 0.01. Base groups are 25-44, NH White, BA or more, top income quartile.

Data Source: 2017 Wave, PSID. Sample: PSID heads and spouses, 25 years and older. Weights: PSID individual cross-section weights. Income quartiles are determined across households using family weights.

Age Race-ethnicity Education Household Income

21

22

23

24

25

26

27

28

Appendix A: Comparison of PSID Sample with Current Population Survey (CPS)

25-44 45-64 65+ 25-44 45-64 65+

Prop (%)., All Ages

(25+)Mean Age

Sample sizes

Overall 37.6 38.1 24.3 51.9 13,150

GenderFemale 37 38 26 51 52 55 52 52.4 7,140Male 39 38 23 49 48 45 48 51.5 6,010

Race and EthncityNH White 33 38 28 64 72 83 71 53.6 7,082NH Black 42 41 17 12 12 7 11 49.2 4,158Hispanic 52 36 13 18 12 7 13 46.5 1,464NH Other 52 32 15 7 4 3 5 47.4 446

EducationHS or less 32 40 28 32 41 44 38 54.3 5,262Some college 39 38 23 25 24 22 24 51.2 3,482BA or more 43 36 22 43 36 34 38 50.1 4,406Sample sizes 6,808 4,392 1,950

25-44 45-64 65+ 25-44 45-64 65+

Prop (%)., All Ages

(25+)Mean Age

Sample sizes

Overall 34.8 40.3 24.9 52.4 101,462

GenderFemale 35 40 25 53 52 54 53 52.5 53,840Male 35 41 25 47 48 46 47 52.4 47,622

Race and EthncityNH White 30 41 29 60 70 80 69 54.3 65,151NH Black 38 43 20 12 11 9 11 50.6 11,378Hispanic 49 38 13 19 12 7 13 46.8 15,976NH Other 47 37 16 10 7 5 8 48.1 8,957

EducationHS or less 29 41 30 30 38 44 37 55.0 38,019Some college 35 41 23 28 28 25 27 51.8 27,560BA or more 40 39 21 42 35 31 36 50.4 35,883Sample sizes 38,732 40,401 22,329

Table A-2. Percent of Demographic and SES Groups by Age across Age Categories & Mean Age for CPS Sample

Data Source: PSID, 2017 Wave. Sample: Household heads and spouses, ages 25 and older. Weights: PSID individual cross-section weights.

Data Source: CPS, 2017 Annual Social & Economic Supplement. Sample: Household heads and spouses, ages 25 and older. Weights: ASEC weights.

Table A-1. Percent of Demographic and SES Groups by Age across Age Categories & Mean Age for PSID SamplePercent across Age Categories Percent within Age Categories

Percent across Age Categories Percent within Age Categories

29

Appendix B: Formula for the DeCaprio et al. Model and Corresponding Risk Factors in the PSID In Table B-1, we provide the variable names and question wording from the 2017 Wave of the PSID for these risk factors. Table B-2 displays the variables used and coefficient estimates for the DeCaprio et al. Model7 along with the corresponding health-related risk factors that are avail-able in the PSID.

PSID staff has compared self-reports about health conditions to the gold-standard for self-reported health conditions, the National Health Interview Survey (NHIS), to evaluate the quality of the PSID data. The PSID estimates for nearly all measures are close to those in the NHIS, a personal household interview study that has been used by the National Center for Health Statistics to monitor Americans’ health since 1957. PSID estimates also show time trends consistent with those from the NHIS (43). There are two exceptions. Chronic kidney disease is twice as common in the NHIS than in the PSID, in which 1% report kidney disease (44). The PSID information about kidney disease, unlike all other conditions we examine, comes from reports about “other serious chronic conditions,” without a specific prompt for kidney-related conditions. The NHIS infor-mation is obtained from a specific question about “weak or failing kidneys,” and specific questions are more likely to elicit these reports. The other exception is the prevalence of “neurocognitive conditions,” which we indicate by whether sample members were ever told by a doctor or health professional that they had “permanent loss of memory or mental ability.” However, the PSID self-reported measure has external validity as demonstrated by its consistent positive association with the Eight Item Interview to Differentiate Aging and Dementia Screen (AD8) (45). Though Insolera & Freedman (43) validate estimates of obesity (BMI ≥ 30) with NHIS, the estimates of severe obesity (BMI ≥ 40) in Table 2 match those in the BRFSS. Rates of hypertension by race-ethnicity in the PSID match crude rates from NHANES almost exactly (34). As noted in the text, the PSID does not have all of the variables that DeCaprio et al.(23) used in the estimation of the logistic regression for this Survey Model. In our implementation of the model, we assumed that the indicators for the risk factors not collected in the PSID had a value of 0. As we note in the text, this means that our estimates of the vulnerability index scores of individuals in the PSID understate their risk of serious disease. The extent of this understatement and how the extent of understatement will vary in the population depends on both the prevalence of the particular risk factors in the population and their marginal impacts on the vulnerability index score, which is determined by the coefficients on these variables. The following conditions are not included: sickle cell disease (SCD), hemodialysis, pneu-monia, rheumatic heart disease, and liver disease (cirrhosis of the liver). The prevalence and/or incidence of most of these conditions is low. SCD affects approximately 100,000 Americans. It is more common among Blacks, than other race-ethnic groups, but occurs among under 0.3% of Black births. (46). In 2017, 746,557 Americans had kidney failure and needed dialysis or a kidney transplant to survive (47). About 250,000 people in the United States seek care each year in a hospital due to pneumonia (48). Finally, the incidence of rheumatic fever is less than 2 cases per

7 The first two columns of Table B-1 are taken from Table 4 of DeCaprio et al. (2020) for their Survey Model. CCSR codes are provided for each of the conditions listed in the model.

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100,000 school-age children and most cases occur in children 5 – 15 years of age (49). The prev-alence of liver disease is higher. About 4.5 million adults are diagnosed with liver disease, with a prevalence of 1.8% (50). Thus, with the exception liver disease (cirrhosis of the liver), the risk factors in the DeCaprio et al. model, but not in the PSID, are of very low prevalence and/or inci-dence. Liver disease has a very low coefficient estimate and a small interaction effect with age. Sickle cell anemia, which has a fairly sizeable impact in the calculation of the index for NH Blacks at ages up to around 50 has a low prevalence in the population. However, because the prevalence of Sickle cell anemia cannot be determined for our PSID sample, the values for the vulnerability index in the PSID data will be (slightly) understated, on average, for NH Blacks and some His-panics.

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Table B-1. Health-Related Risk Factors and Other Variables, Variable Names, and Question Wording from the 2017 Wave of the PSID

Risk Factor/Variable PSID Variable Question Wording Age ER34504 What is your current age? Gender ER32000 What is your gender? Past Hospitalization ER68511, ER69638 Were you a patient in a hospital overnight or longer at any time during 2016? Lung Disease ER68454, ER69581 Has a doctor or other health professional ever told you that you had chronic lung disease

such as bronchitis or emphysema? Asthma ER68449, ER69576 Has a doctor or other health professional ever told you that you had asthma? Severe Obesity ER68568, ER68569,

ER69695, ER69696, ER68566, ER68567

Constructed BMI using height and weight variables and designating severely obese if BMI ≥ 40

Diabetes ER68459, ER69586 Has a doctor or other health professional ever told you that you had diabetes or high blood sugar?

Hypertension ER68444, ER69571 Has a doctor or other health professional ever told you that you had high blood pressure or hypertension?

Heart Disease ER68439, ER69566 Has a doctor or other health professional ever told you that you had coronary heart disease, angina, congestive heart failure?

Heart Attack ER68433, ER69560 Has a doctor or other health professional ever told you that you had a heart attack? Stroke ER68427, ER69554 Has a doctor or other health professional ever told you that you had a stroke? Kidney Disease ER68498, ER69625 Is there any other serious, chronic condition that a doctor or other health professional ever

told you had? [ 3: Kidney disease] Cancer ER68479, ER69606 Has a doctor or other health professional ever told you that you had cancer or a malignant

tumor? Neurological condition ER68469, ER69596 Has a doctor or other health professional ever told you that you had permanent loss of

memory or mental ability?

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Table B-2. Risk Factors & Other Conditions from the DeCaprio et al. Model

Variables in DeCaprio et al. Survey Model Coefficient Estimate

Variables available in the PSID

intercept -6.740 Age 0.041 Age Male 0.171 Male Prior Admissions 0.682 Hospitalized Prior ER Visits 0.413 Chronic obstructive pulmonary disease (COPD) or emphysema, cystic fibrosis, or chronic bronchitis 1.167 Lung Disease Asthma 1.393 Asthma Obesity (BMI ≥ 40) 0.935 Severe Obesity (BMI ≥ 40) Diabetes (other than when you were preg-nant) 0.096 Diabetes Hypertension (or high blood pressure) 0.832 Hypertension Congestive Heart Failure 0.982 Heart Disease Heart attack 0.159 Heart Attack Rheumatic heart disease 0.788 Stroke 0.285 Stroke Sickle cell anemia/HIV infection/Transplant 2.582 Chronic kidney disease 0.966 Kidney Disease Hemodialysis 1.369 Liver disease 0.055 Pneumonia, acute bronchitis, influenza or other acute respiratory infection 0.696 Cancer 1.091 Cancer Neurocognitive conditions 0.294 Neurological Condition Pregnancy, childbirth & puerperium 0.789 COPD x Age -0.002 Lung Disease x Age Asthma x Age -0.015 Asthma x Age Obesity x Age -0.004 Severe Obesity x Age Diabetes x Age 0.000 Diabetes x Age Hypertension x Age 0.005 Hypertension x Age Congestive heart failure x Age -0.007 Heart Disease x Age Myocardial infarction x Age 0.003 Heart Attack x Age Rheumatic heart disease x Age -0.008 Stroke x Age -0.003 Stroke x Age Sickle cell/HIV/Transplant x Age -0.028 Chronic kidney disease x Age -0.008 Kidney Disease x Age Hemodialysis x Age -0.018 Liver disease x Age 0.001 Pneumonia, acute bronchitis, influenza or other acute respiratory infection x Age -0.005 Cancer x Age -0.009 Cancer x Age Neurocognitive conditions x Age 0.004 Neurological Condition x Age Pregnancy, childbirth & puerperium x Age -0.003

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Appendix C: Risk Factors by Subgroup and Differences in VI in Midlife by Two Dimensions of Population Subgroup Membership

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Appendix D: Calculation of VI among COVID-19 Hospitalized Patients

To calculate VI based on the average conditions of COVID-19 hospitalized patients overall and by age group (18-49, 50-64, 65+), we use the prevalence of preexisting conditions of hospi-talized patients reported in Garg et al. (20) and apply these prevalence rates to our calculation of VI described in Equation (4) in the text. We match each condition with its corresponding condition in DeCaprio et al. (23) as shown in Appendix Table D-1. For the two demographic factors in DeCaprio et al. (23), age and gender, we apply the median age overall and in each age group from the PSID and the percent of all hospitalized patients who are male (54%) from Garg et al. (20) to each age group. There are several preexisting conditions in Garg et al. (20) that are not in the DeCaprio et al. model and we do not include these in our calculations of VI. Garg et al. (20) on preexisting conditions was only from March 2020 and for 178 hospital-ized patients (12% of hospitalized adults), as these data require time-consuming abstractions from hospital medical charts. Though the sample in Garg et al. (20) is small, it has the advantage that preexisting conditions are reported overall and by age group. The incidence of preexisting health conditions for adults is very similar to that data reported for the week ending June 6, 2020, although June data are not reported by age (21).

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Underlying Conditions Garg et al. (2020)

Corresponding Condition DeCaprio et al. (2020)

Hypertension HypertensionObesity (BMI>=30) Severe Obesity (BMI>=40)Chronic metabolic disease No exact match (excluded) Diabetes mellitus DiabetesChronic lung disease Asthma Asthma Chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) or emphysema, cystic fibrosis, or chronic bronchitis

Cardiovascular disease (not hypertension) Coronary artery disease Rheumatic and other heart disease Congestive heart failure Congestive heart failureNeurologic disease StrokeRenal disease Chronic kidney diseaseImmunosuppressive condition No exact match (excluded)Gastrointestinal/Liver disease Liver diseaseBlood disorder No exact match (excluded)Rheumatologic/Autoimmune disease

No exact match (excluded)Notes: Pregnancy is excluded because we do not use it in the PSID estimation. Garg et al. (2020) reports that 30% of obsese patients are severely obese and we apply the percentage uniformly across age groups. We treat all reports of chronic lung disease that are not asthma as being included in COPD, emphysema, cystic fibrosis, or chronic bronchitis. We treat all reports of cardiovascular disease (not hypertension) that are not congestive heart disease as coronary artery disease. We have tested using applying the coefficients for neurocognitive conditions instead of stroke as the corresponding condition for neurologic disease and the results are unchanged. We have also tested using sickle cell/HIV/transplant in DeCaprio et al. (2020) as a match for immunosuppresive conditions in Garg et al. (2020). Again the results are very similar.

Appendix Table D-1. Correspondence between Garg et al. (2020) and DeCaprio et al. (2020)